SoroushVahidi/scidocs huggingface dataset:
Deep Representation Learning for Human Motion Prediction and Classification
(2017)

Generative models of 3D human motion are often restricted to a small number of activities and can therefore not generalize well to novel movements or applications. In this work we propose a deep learning framework for human motion capture data that learns a generic representation from a large corpus of motion capture data and generalizes well to new, unseen, motions. Using an encoding-decoding network that learns to predict future 3D poses from the most recent past, we extract a feature representation of human motion. Most work on deep learning for sequence prediction focuses on video and speech. Since skeletal data has a different structure, we present and evaluate different network architectures that make different assumptions about time dependencies and limb correlations. To quantify the learned features, we use the output of different layers for action classification and visualize the receptive fields of the network units. Our method outperforms the recent state of the art in skeletal motion prediction even though these use action specific training data. Our results show that deep feedforward networks, trained from a generic mocap database, can successfully be used for feature extraction from human motion data and that this representation can be used as a foundation for classification and prediction.
On Human Motion Prediction Using Recurrent Neural Networks
(2017)

Human motion modelling is a classical problem at the intersection of graphics and computer vision, with applications spanning human-computer interaction, motion synthesis, and motion prediction for virtual and augmented reality. Following the success of deep learning methods in several computer vision tasks, recent work has focused on using deep recurrent neural networks (RNNs) to model human motion, with the goal of learning time-dependent representations that perform tasks such as short-term motion prediction and long-term human motion synthesis. We examine recent work, with a focus on the evaluation methodologies commonly used in the literature, and show that, surprisingly, state of the art performance can be achieved by a simple baseline that does not attempt to model motion at all. We investigate this result, and analyze recent RNN methods by looking at the architectures, loss functions, and training procedures used in state-of-the-art approaches. We propose three changes to the standard RNN models typically used for human motion, which results in a simple and scalable RNN architecture that obtains state-of-the-art performance on human motion prediction.
Trial for RDF: adapting graph query languages for RDF data
(2013)

Querying RDF data is viewed as one of the main applications of graph query languages, and yet the standard model of graph databases -- essentially labeled graphs -- is different from the triples-based model of RDF. While encodings of RDF databases into graph data exist, we show that even the most natural ones are bound to lose some functionality when used in conjunction with graph query languages. The solution is to work directly with triples, but then many properties taken for granted in the graph database context (e.g., reachability) lose their natural meaning.
 Our goal is to introduce languages that work directly over triples and are closed, i.e., they produce sets of triples, rather than graphs. Our basic language is called TriAL, or Triple Algebra: it guarantees closure properties by replacing the product with a family of join operations. We extend TriAL with recursion, and explain why such an extension is more intricate for triples than for graphs. We present a declarative language, namely a fragment of datalog, capturing the recursive algebra. For both languages, the combined complexity of query evaluation is given by low-degree polynomials. We compare our languages with relational languages, such as finite-variable logics, and previously studied graph query languages such as adaptations of XPath, regular path queries, and nested regular expressions; many of these languages are subsumed by the recursive triple algebra. We also provide examples of the usefulness of TriAL in querying graph and RDF data.
Clinical application of the Hybrid Assistive Limb (HAL) for gait training—a systematic review
(2015)

OBJECTIVE
The aim of this study was to review the literature on clinical applications of the Hybrid Assistive Limb system for gait training.


METHODS
A systematic literature search was conducted using Web of Science, PubMed, CINAHL and clinicaltrials.gov and additional search was made using reference lists in identified reports. Abstracts were screened, relevant articles were reviewed and subject to quality assessment.


RESULTS
Out of 37 studies, 7 studies fulfilled inclusion criteria. Six studies were single group studies and 1 was an explorative randomized controlled trial. In total, these studies involved 140 participants of whom 118 completed the interventions and 107 used HAL for gait training. Five studies concerned gait training after stroke, 1 after spinal cord injury (SCI) and 1 study after stroke, SCI or other diseases affecting walking ability. Minor and transient side effects occurred but no serious adverse events were reported in the studies. Beneficial effects on gait function variables and independence in walking were observed.


CONCLUSIONS
The accumulated findings demonstrate that the HAL system is feasible when used for gait training of patients with lower extremity paresis in a professional setting. Beneficial effects on gait function and independence in walking were observed but data do not allow conclusions. Further controlled studies are recommended.
ValueCharts: analyzing linear models expressing preferences and evaluations
(2004)

In this paper we propose ValueCharts, a set of visualizations and interactive techniques intended to support decision-makers in inspecting linear models of preferences and evaluation. Linear models are popular decision-making tools for individuals, groups and organizations. In Decision Analysis, they help the decision-maker analyze preferential choices under conflicting objectives. In Economics and the Social Sciences, similar models are devised to rank entities according to an evaluative index of interest. The fundamental goal of building models expressing preferences and evaluations is to help the decision-maker organize all the information relevant to a decision into a structure that can be effectively analyzed. However, as models and their domain of application grow in complexity, model analysis can become a very challenging task. We claim that ValueCharts will make the inspection and application of these models more natural and effective. We support our claim by showing how ValueCharts effectively enable a set of basic tasks that we argue are at the core of analyzing and understanding linear models of preferences and evaluation.
Artificial Neural Networks to Forecast Air Pollution 221 Artificial Neural Networks to Forecast Air Pollution
(2010)

European laws concerning urban and suburban air pollution requires the analysis and implementation of automatic operating procedures in order to prevent the risk for the principal air pollutants to be above alarm thresholds (e.g. the Directive 2002/3/EC for ozone or the Directive 99/30/CE for the particulate matter with an aerodynamic diameter of up to 10 μm, called PM10). As an example of European initiative to support the investigation of air pollution forecast, the COST Action ES0602 (Towards a European Network on Chemical Weather Forecasting and Information Systems) provides a forum for standardizing and benchmarking approaches in data exchange and multi-model capabilities for air quality forecast and (near) real-time information systems in Europe, allowing information exchange between meteorological services, environmental agencies, and international initiatives. Similar efforts are also proposed by the National Oceanic and Atmospheric Administration (NOAA) in partnership with the United States Environmental Protection Agency (EPA), which are developing an operational, nationwide Air Quality Forecasting (AQF) system. Critical air pollution events frequently occur where the geographical and meteorological conditions do not permit an easy circulation of air and a large part of the population moves frequently between distant places of a city. These events require drastic measures such as the closing of the schools and factories and the restriction of vehicular traffic. Indeed, many epidemiological studies have consistently shown an association between particulate air pollution and cardiovascular (Brook et al., 2007) and respiratory (Pope et al., 1991) diseases. The forecasting of such phenomena with up to two days in advance would allow taking more efficient countermeasures to safeguard citizens’ health. Air pollution is highly correlated with meteorological variables (Cogliani, 2001). Indeed, pollutants are usually entrapped into the planetary boundary layer (PBL), which is the lowest part of the atmosphere and has behaviour directly influenced by its contact with the ground. It responds to surface forcing in a timescale of an hour or less. In this layer, physical quantities such as flow velocity, temperature, moisture and pollutants display rapid fluctuations (turbulence) and vertical mixing is strong. Different automatic procedures have been developed to forecast the time evolution of the concentration of air pollutants, using also meteorological data. Mathematical models of the 10
Grace: safe multithreaded programming for C/C++
(2009)

The shift from single to multiple core architectures means that programmers must write concurrent, multithreaded programs in order to increase application performance. Unfortunately, multithreaded applications are susceptible to numerous errors, including deadlocks, race conditions, atomicity violations, and order violations. These errors are notoriously difficult for programmers to debug.
 This paper presents Grace, a software-only runtime system that eliminates concurrency errors for a class of multithreaded programs: those based on fork-join parallelism. By turning threads into processes, leveraging virtual memory protection, and imposing a sequential commit protocol, Grace provides programmers with the appearance of deterministic, sequential execution, while taking advantage of available processing cores to run code concurrently and efficiently. Experimental results demonstrate Grace's effectiveness: with modest code changes across a suite of computationally-intensive benchmarks (1-16 lines), Grace can achieve high scalability and performance while preventing concurrency errors.
Structural Data De-anonymization: Quantification, Practice, and Implications
(2014)

In this paper, we study the quantification, practice, and implications of structural data (e.g., social data, mobility traces) De-Anonymization (DA). First, we address several open problems in structural data DA by quantifying perfect and (1-ε)-perfect structural data DA}, where ε is the error tolerated by a DA scheme. To the best of our knowledge, this is the first work on quantifying structural data DA under a general data model, which closes the gap between structural data DA practice and theory. Second, we conduct the first large-scale study on the de-anonymizability of 26 real world structural datasets, including Social Networks (SNs), Collaborations Networks, Communication Networks, Autonomous Systems, and Peer-to-Peer networks. We also quantitatively show the conditions for perfect and (1-ε)-perfect DA of the 26 datasets. Third, following our quantification, we design a practical and novel single-phase cold start Optimization based DA} (ODA) algorithm. Experimental analysis of ODA shows that about 77.7% - 83.3% of the users in Gowalla (.2M users and 1M edges) and 86.9% - 95.5% of the users in Google+ (4.7M users and 90.8M edges) are de-anonymizable in different scenarios, which implies optimization based DA is implementable and powerful in practice. Finally, we discuss the implications of our DA quantification and ODA and provide some general suggestions for future secure data publishing.
Technology , globalization , and international competitiveness : Challenges for developing countries
(2007)

This paper traces the role of technology in economic growth and competitiveness, summarizes the strategies of the fastest growing economies over the last 50 years from the perspective of their technology strategy, summarizes some of the key global trends which are making it more difficult for developing countries to replicate the fast growth experience of the countries mentioned, and traces the impact of the rise of China on developing countries. The main argument of this paper is that technology is an increasingly important element of globalisation and of competitiveness and that the acceleration in the rate of technological change and the pre-requisites necessary to participate effectively in globalisation are making it more difficult for many developing countries to compete. Section 2 gives a long-term perspective on technology and economic growth. Section 3 presents a global overview of changes in regional competitiveness as revealed by economic growth. Section 4 identifies some of the high performers in the last 50 years and reviews the strategies of the high performing East Asian economies comprising the well known “gang of four”, plus three South East Asian countries. Section 5 reviews the strategies of the BRICM countries, the largest developing country economies (Brazil, Russia, India, China and Mexico). It also argues that it is harder for developing countries to replicate the success of the high performing East Asian countries for two main reasons. One relates to new elements in the global competitive environment. These are summarized in section 6. The other is the rapid rise of China (and to a lesser extent India). This is covered in Section 7, which also includes a preliminary analysis of the effects of the rapid rise of China on the rest of the world. Finally, Section 8 draws some conclusions. Developing countries must develop more technological capability and greater flexibility to succeed in the more demanding and asymmetric global environment. It is likely that the pressures of globalisation and greater international competition generate strong protectionist retrenchment in both developed and developing countries. These should be resisted. The world as a whole will be better off if developed countries focus on increasing their flexibility to adjust to changing comparative advantage resulting from rapid technical change, and developing countries focus on increasing their education, infrastructure, and technological capability. There remain however large asymmetries in the global system and greater efforts need to be made to provide some global balancing and transfer mechanisms.
A Novel Multi-task Deep Learning Model for Skin Lesion Segmentation and Classification
(2017)

In this study, a multi-task deep neural network is proposed for skin lesion analysis. The proposed multi-task learning model solves different tasks (e.g., lesion segmentation and two independent binary lesion classifications) at the same time by exploiting commonalities and differences across tasks. This results in improved learning efficiency and potential prediction accuracy for the task-specific models, when compared to training the individual models separately. The proposed multi-task deep learning model is trained and evaluated on the dermoscopic image sets from the International Skin Imaging Collaboration (ISIC) 2017 Challenge “Skin Lesion Analysis towards Melanoma Detection”, which consists of 2000 training samples and 150 evaluation samples. The experimental results show that the proposed multi-task deep learning model achieves promising performances on skin lesion segmentation and classification. The average value of Jaccard index for lesion segmentation is 0.724, while the average values of area under the receiver operating characteristic curve (AUC) on two individual lesion classifications are 0.880 and 0.972, respectively.
Multi-Task Learning with Multi-View Attention for Answer Selection and Knowledge Base Question Answering
(2019)

Answer selection and knowledge base question answering (KBQA) are two important tasks of question answering (QA) systems. Existing methods solve these two tasks separately, which requires large number of repetitive work and neglects the rich correlation information between tasks. In this paper, we tackle answer selection and KBQA tasks simultaneously via multi-task learning (MTL), motivated by the following motivations. First, both answer selection and KBQA can be regarded as a ranking problem, with one at text-level while the other at knowledge-level. Second, these two tasks can benefit each other: answer selection can incorporate the external knowledge from knowledge base (KB), while KBQA can be improved by learning contextual information from answer selection. To fulfill the goal of jointly learning these two tasks, we propose a novel multi-task learning scheme that utilizes multi-view attention learned from various perspectives to enable these tasks to interact with each other as well as learn more comprehensive sentence representations. The experiments conducted on several real-world datasets demonstrate the effectiveness of the proposed method, and the performance of answer selection and KBQA is improved. Also, the multi-view attention scheme is proved to be effective in assembling attentive information from different representational perspectives.
A Unified Approach for Learning the Parameters of Sum-Product Networks
(2016)

• We present a unified approach for learning the parameters of Sum-Product networks (SPNs). • We construct a more efficient factorization of complete and decomposable SPN into a mixture of trees, with each tree being a product of univariate distributions. • We show that the MLE problem for SPNs can be formulated as a signomial program. • We construct two parameter learning algorithms for SPNs by using sequential monomial approximations (SMA) and the concave-convex procedure (CCCP). Both SMA and CCCP admit multiplicative weight updates. • We prove the convergence of CCCP on SPNs.
A survey on skin detection in colored images
(2018)

Color is an efficient feature for object detection as it has the advantage of being invariant to changes in scaling, rotation, and partial occlusion. Skin color detection is an essential required step in various applications related to computer vision. The rapidly-growing research in human skin detection is based on the premise that information about individuals, intent, mode, and image contents can be extracted from colored images, and computers can then respond in an appropriate manner. Detecting human skin in complex images has proven to be a challenging problem because skin color can vary dramatically in its appearance due to many factors such as illumination, race, aging, imaging conditions, and complex background. However, many methods have been developed to deal with skin detection problem in color images. The purpose of this study is to provide an up-to-date survey on skin color modeling and detection methods. We also discuss relevant issues such as color spaces, cost and risks, databases, testing, and benchmarking. After investigating these methods and identifying their strengths and limitations, we conclude with several implications for future direction.
The evolution of cooperation.
(1981)

Cooperation in organisms, whether bacteria or primates, has been a difficulty for evolutionary theory since Darwin. On the assumption that interactions between pairs of individuals occur on a probabilistic basis, a model is developed based on the concept of an evolutionarily stable strategy in the context of the Prisoner's Dilemma game. Deductions from the model, and the results of a computer tournament show how cooperation based on reciprocity can get started in an asocial world, can thrive while interacting with a wide range of other strategies, and can resist invasion once fully established. Potential applications include specific aspects of territoriality, mating, and disease.
Visual Graphs from Motion (VGfM): Scene understanding with object geometry reasoning
(2018)

Recent approaches on visual scene understanding attempt to build a scene graph – a computational representation of objects and their pairwise relationships. Such rich semantic representation is very appealing, yet difficult to obtain from a single image, especially when considering complex spatial arrangements in the scene. Differently, an image sequence conveys useful information using the multi-view geometric relations arising from camera motions. Indeed, object relationships are naturally related to the 3D scene structure. To this end, this paper proposes a system that first computes the geometrical location of objects in a generic scene and then efficiently constructs scene graphs from video by embedding such geometrical reasoning. Such compelling representation is obtained using a new model where geometric and visual features are merged using an RNN framework. We report results on a dataset we created for the task of 3D scene graph generation in multiple views.
Chip design of a 12-bit 5MS/s fully differential SAR ADC with resistor-capacitor array DAC technique for wireless application
(2015)

A 1.8-V 12-bit 5MS/s successive approximation register (SAR) analog-to-digital converter (ADC) implemented in TSMC 0.18-um CMOS process is presented. To reduce DAC switching energy and chip area, a hybrid resistor-capacitor DAC is applied. To save energy, asynchronous control logic to drive the ADC is used. A pre-amplifier based comparator circuit is built to reduce the kickback noise from the dynamic latch designs. With 1.8 V supply voltage and 5.0 MHz sampling rate, measured results achieve -0.55/0.72 LSB (Least Significant Bit) of DNL (differential nonlinearity) and -0.78/0.92 LSB of integral nonlinearity (INL) respectively, and 10.76 bits of an effective number of bits (ENOB) at 1MHz input frequency. The chip area is 0.83 mm2 including pads and the power consumption is 490μW for optical and wireless communications.
Captcha as graphical passwords-enhanced with video-based captcha for secure services
(2015)

CAPTCHAs, also known as reverse Turing tests are real-time assessments that are commonly used by programs to tell humans and machines apart. This can be achieved by assigning and assessing or evaluating hard AI problems such that these problems could only be solved easily by human but not by machines. A new security approach based on hard AI problems and Captcha technology is known as Captcha as gRaphical Passwords (CaRP). This scheme can address many security problems such as dictionary attacks, online guessing attacks and shoulder-surfing attacks etc. In this paper, we present an enhanced security for the CaRP scheme i,e CaRP with motion-based Captcha. The motion can be done by using video. The movement of Captcha ensures high security over the normal CaRP scheme. Video-based captcha can provide a great challenge for humans in exploiting the remarkable perceptual abilities or skills of humans to unravel structure-from-motion. The scheme can resist attack caused due to moving object detection. Thus it can thwart many attacks, can enforce more security and it stipulates security along with usability to legitimate users in dealing with real time applications.